Panel Data Analysis: A nontechnical introduction for marketing researchers

DOI:	https://doi.org/10.1007/978-3-319-05542-8_19-1
URL:	https://link.springer.com/referenceworkentry/10.10...
Weitere URL:	https://research.rug.nl/en/publications/panel-data...
Dokumenttyp:	Buchkapitel
Erscheinungsjahr Online:	2021
Buchtitel:	Handbook of market research
Seitenbereich:	1-58
Herausgeber:	Homburg, Christian ; Klarmann, Martin ; Vomberg, Arnd
Ort der Veröffentlichung:	Cham
Verlag:	Springer
ISBN:	978-3-319-05542-8
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Fakultät für Betriebswirtschaftslehre > Empirische Forschungsmethoden (Juniorprofessur) (Vomberg 2014-2018)
Fachgebiet:	650 Management 330 Wirtschaft
Abstract:	The analysis of panel data is now part of the standard repertoire of marketers and marketing researchers. Compared to the analysis of cross-sectional data, panel data allow marketers to alleviate endogeneity concerns when linking an independent variable (e.g., price) to an outcome variable (e.g., sales volume). The more accurate estimates that result from panel data analysis help improve marketers’ decision-making in focal areas such as price setting and marketing budget allocation. Besides, panel data allow marketers to track customer behavior changes and distinguish real loyalty effects (i.e., same customer repeatedly buys a brand) from spurious effects (i.e., the same number of, but each time different set of, customers buys a brand). This chapter provides a nontechnical introduction to panel data analysis. Marketers will learn how to manage and analyze panel datasets in Stata. They will learn about the focal panel data estimators (pooled OLS, fixed effects, and random effects estimator), their underlying assumptions, advantages, and pitfalls. Besides, we introduce the between effects estimator, the combined approach, the Hausman-Taylor approach, and the first differences estimator as further techniques to analyze panel data. Finally, readers will receive an introduction to advanced topics such as dynamic panel models, panel data multilevel modeling, and using panel data to address measurement errors.

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